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Abstract

Background

Counselor behaviors that mediate the efficacy of motivational interviewing (MI) are
not well understood, especially when applied to health behavior promotion. We hypothesized
that client change talk mediates the relationship between counselor variables and
subsequent client behavior change.

Methods

Purposeful sampling identified individuals from a prospective randomized worksite
trial using an MI intervention to promote firefighters’ healthy diet and regular exercise
that increased dietary intake of fruits and vegetables (n = 21) or did not increase intake of fruits and vegetables (n = 22). MI interactions were coded using the Motivational Interviewing Skill Code
(MISC 2.1) to categorize counselor and firefighter verbal utterances. Both Bayesian
and frequentist mediation analyses were used to investigate whether client change
talk mediated the relationship between counselor skills and behavior change.

Conclusion

Motivational interviewing is a resource- and time-intensive intervention, and is currently
being applied in many arenas. Previous research has identified the importance of counselor
behaviors and client change talk in the treatment of substance use disorders. Our
results indicate that similar mechanisms may underlie the effects of MI for dietary
change. These results inform MI training and application by identifying those processes
critical for MI success in health promotion domains.

Keywords:

Background

The firefighter stereotype likely elicits an image of a strong hero rescuing individuals
from burning buildings. Yet firefighters’ health profiles mirror that of other workers
and include an unhealthy diet, a lack of regular physical activity, and being overweight/obese
[1]. In addition, occupational hazards increase firefighters’ risk for cancer, heart
disease, and musculoskeletal injuries [2-6]. Therefore, firefighters potentially benefit from health promotion programs; however,
previous interventions targeting firefighters’ diet and exercise have not been particularly
successful [7].

Overview of PHLAME

The PHLAME (Promoting Healthy Lifestyles: Alternative Models’ Effects) study was a
randomized prospective trial of two paradigms to achieve healthy nutrition and regular
physical activity. Firefighters from five departments were randomized to one of three
conditions: 1) a team-centered peer taught curriculum, which used firefighters’ inherent
team structure to promote task cohesion and social norms of healthy diet and exercise;
2) one-on-one counselor-led motivational interviewing (MI) sessions; and 3) a control,
assessment-only format. The study’s details, one-year and longitudinal outcomes, and
team-centered program’s mediation effects have been reported elsewhere [8-10]. Both interventions achieved moderate effect sizes for improving diet and physical
activity [8], and PHLAME is listed on the Cancer Control P.L.A.N.E.T. website for evidence-based
programs for both promoting healthy nutrition and enhancing physical activity (http://cancercontrolplanet.cancer.gov/webcite). This project reports findings concerning the mechanism of motivational interviewing’s
efficacy in the PHLAME intervention.

Motivational interviewing (MI)

Motivational Interviewing emphasizes using autonomous motivation described in self-determination
theory to elicit change [11,12]. Self-determination theory [13] suggests that two types of motivation exist—autonomous, or motivations to change
coming from within the individual, and controlled, or motivations to change coming
from outside pressures. Autonomous motivation to change better elicits and sustains
change over time than controlled motivation, because the motivation to change is self-driven,
rather than driven by the desires of others. In traditional behavior modification
counseling, the provider educates the client about his or her unhealthy action(s),
the potential adverse consequences and advises the individual to change their behavior.
Although intuitively compelling, evidence of adverse effects might seem sufficient
to overcome robust defense mechanisms and motivate change, this external motivation
model has limited effectiveness [14]. Motivational interviewing (MI) is designed to establish an individual’s autonomous
motivation and allows her or him to define a personally relevant change process [11,12]. A counselor using an MI approach elicits, rather than imposes upon, the client’s
own reasons for change (autonomous motivation); assists in resolving ambiguity about
the decision; collaborates on any change plans; and honors the client’s autonomy.
Specific MI skills are well described and include expressing empathy, using accurate
reflective listening, asking open-ended questions that elicit reasons to change, rolling
with resistance, and affirming the client’s strengths, abilities, and self efficacy
[12].

The initial research with MI established its efficacy with addiction and substance
abuse [15,16], followed by extending the technique as a means to promote healthy actions [14,17]. More than 200 MI trials have been published and compiled in reviews [18-24]. In recent years, MI has been applied to new behavioral domains (e.g., schizophrenia
[25], childhood obesity [26]), and investigators also have examined MI’s inner workings and theoretical structure
by establishing the relationship among counselor actions, client comments, and subsequent
behavioral outcomes, thus potentially enhancing the efficacy of MI interventions and
its application to other domains.

MI & health promotion

MI has been successful in substance use intervention research [22,27-29], yet its use in health promotion research has not yet been extensively studied [22,30]. The psychological processes underlying cessation of negative behaviors may differ
from promotion of positive behaviors; likewise, the psychological processes underlying
addictive versus non-addictive behavioral change may differ [31].

For example, using MI for addictive behavior modification, such as smoking or alcohol
cessation, requires overcoming psychological and physiological resistance, which potentially makes the process more difficult. Feelings
of denial, ambivalence, and resistance may be stronger for addictive behaviors than
non-addictive behaviors like fruit and vegetable consumption [31]. Indeed, Resnicow et al. [32] found that the major barriers to increasing fruit and vegetable consumption were
more pragmatic (e.g., lack of time and availability, taste preference) than psychological
or physiological. Thus, the psychology underlying abstinence and relapse may be less
relevant to health promotion MI interventions [31]. Finally, the link between cigarette, alcohol, and other substance use and severe
health risks, such as cancers, are identifiable and accessible, whereas the link between fruit
and vegetable consumption and health benefits is probably less accessible and seems less certain. The ease of accessibility and
severity of possible consequences may enhance one’s motivation to quit. It is therefore
important to test MI’s success in health promotion interventions as the strength of
the motivation to change might differ and undercut its effectiveness.

MI’s underlying mechanisms: Client change talk

Client “change talk” (i.e., clients’ utterances of his or her intentions to change)
has emerged as a potential intermediate variable between the application of specific
MI counseling skills and behavior change [33-35]. The role of change talk is consistent with self-perception theory [36], which suggests that individuals more strongly endorse and may act on attitudes they
hear themselves say rather than what others are advising them. Sequential analyses
have provided temporal evidence of counselor techniques eliciting client responses
more strongly than the reverse causal direction of client responses eliciting counselor
techniques [35,37]. However, to date, causal linkage studies only have involved MI use in stopping addictions,
primarily with alcohol use [18,35,37-40], along with gambling [41]. This is the first description of MI mediation applied to promoting healthy dietary
change. Furthermore, we compared the effects of Bayesian and frequentist mediation
analyses. We hypothesize appropriate counselor MI techniques predict increased client
change talk, which in turn predicts increased fruit and vegetable intake.

Methods

PHLAME involvement was voluntary, all information collected was confidential, and
participants provided written informed consent. Data was collected in 2002 through
2004, with coding and analyses performed from 2009 through 2011. The Institutional
Review Board of the Oregon Health & Science University first approved the study in
August of 2000.

Participant selection

Two hundred and two firefighters participated in the PHLAME motivational interviewing
(MI) condition with their MI sessions audiotaped. Participants were assessed at baseline,
the MI intervention began soon after, and follow-up measures were obtained one-year
after baseline. Purposeful sampling (a technique to obtain a non-representative subset
of a larger population to serve a specific purpose) was used to identify approximately
40 individuals for inclusion in these analyses to achieve a range of performance on
the outcome variable—daily fruit and vegetable intake—and be feasibly coded for analysis
within the study budget. Having a sample in which some participants changed and some
did not would hopefully provide sufficient variability on the mediating and outcome
variables so as to increase the ability to determine the mechanisms of the MI intervention.
Forty three participants’ MI sessions were selected for coding, with 21 indentified
as “changers” and 22 indentified as “non-changers.” “Changer” criteria were an increase
in fruit and vegetable intake of at least 50%, an initial fruit and vegetable intake
of less than 8 servings per day, and weight gain less than 5% of initial body weight.
“Non-changer” criteria were an increase or decrease in fruit and vegetable intake
of less than 15% and an initial fruit and vegetable intake of less than 8 servings
per day. Changers and non-changers were equivalent at baseline on age, years as firefighter,
gender, body mass index, general health, intent to eat fruit and vegetables, and autonomous
motivation for diet behavior. Table 1 summarizes and compares the baseline assessments of changers, non-changers, and the
overall sample.

Table 1.Baseline comparison of changers, non-changers and the participant cohort assigned
to the Motivational Interviewing intervention

Motivational interviewing intervention

Firefighters met with one of four MI counselors. Prior to the study, each counselor
completed approximately 90 hours of MI training, including workshops, educational
videotapes, personal coaching from an expert trainer, and practice with standardized
patients. Comparable counselor proficiency was established using the Motivational
Interviewing Skill Code system (MISC 1.0 [43]), which classifies a counselor’s verbal utterances (statements or single thoughts
that end when interrupted or a new statement begins) into mutually exclusive categories.
Results were used to calculate indices of MI performance [44]. An independent MISC coder scored each counselor’s practice tapes to verify proficiency.
Throughout the study, the counselors met periodically to provide mutual support, debrief
challenging interactions, and review MI skills. The structure of their training and
resultant fidelity to MI skill criteria have been reported previously [8,45].

Each MI firefighter met at the station with his or her counselor for a series of at
least four 30–60 minute one-on-one sessions. The first meeting discussed study timeline
and goals and client’s health and values; the second meeting included a review of
the firefighter’s baseline testing results (e.g., dietary indices, fitness measures,
body weight); and the third and fourth sessions continued to focus on achieving the
clients’ objectives relative to the study goals of increasing daily servings of fruits
and vegetables each day, enhancing daily physical activity, and achieving a healthy
body weight. The second MI session was selected for coding to capture the client’s
reaction to his or her test results and potential client change talk in response to
those results.

Coders and coding

Two research assistants at Oregon Health & Sciences University were trained to use
the Motivational Interviewing Skills Coding System (MISC 2.1) in approximately 80
hours over eight weeks. The MISC instrument was developed and refined as a method
for evaluating the content of motivational interviewing. The MISC manual, including
its rationale, development, and explicit instructions, is available at http://casaa.unm.edu/download/misc.pdfwebcite[44]. The MISC has 15 mutually exclusive utterance categories for the counselor’s speech,
four categories for client speech, and six “global” scores for the overall interaction.

For accurate coding, the audiotapes were transcribed. Following training, the 43 selected
participants’ tapes and transcripts were randomly assigned to the coders, who were
unaware of the participant’s identity and outcomes. Coders then both listened to and
reviewed the transcripts using a modified MISC coding system. Coding an interaction
took three times the length of a recorded session, thus a 45-minute session required
more than two hours to code.

For the counselor behaviors, utterance codes for the 15 major categories were tallied
and MI-consistent and MI-inconsistent behaviors were summed for each interaction.
MI-consistent behaviors were the combined counts of the following utterance categories: affirm, advise with
permission, emphasize control, ask open question, reflect, reframe, and support. MI-inconsistent behaviors consisted of summing the following utterance categories: confront, advise without
permission, direct, raise concern without permission, and warn [44].

Coders also rated the overall interaction on the following five counselor global categories:
evocation (counselor elicits client change talk), collaboration (counselor supports and explores the client’s own concerns), autonomy-support (counselor emphasizes the client’s freedom of choice), empathy (counselor is interested in client’s perceptions, situations, and feelings), and
direction (counselor directs client to targeted behaviors) on a 5-point scale, whereby 1 indicated
the lowest degree of that construct and 5 indicated the highest level. An MI spirit construct was calculated based on averaging the collaboration, evocation, and autonomy-support
items [44].

Firefighters’ utterances were coded based on MISC client change talk categories and
identified as total positive client change talk and total client sustain talk, which indicated intentions to change behaviors and sustain the status quo, respectively.
The mutually exclusive categories included the positive and negative valences of the
categories of client commitment language, client taking steps, and client change talk.
Total positive client change talk was the sum of positive client change talk, positive client commitment language,
and positive client taking steps, and total client sustain talk was the sum of sustain client change talk, sustain client commitment language, and
sustain client taking steps. Coders also provided an overall global rating (on a 7-point
scale, 7 indicating the highest level) of the clients’ engagement and self-discovery,
called self-exploration.

The outcome behavior was dietary change in fruit and vegetable intake, assessed by
number of servings of fruits and vegetables per day, using a previously validated
self-report instrument [46]. The change score was computed by subtracting the baseline daily fruit/vegetable
intake score from the one-year post-treatment fruit/vegetable intake score, with higher
scores indicating greater increase in fruit and vegetable intake.

Inter-rater reliability

Ten percent of sessions (4 interactions) were randomly selected and coded by both
research assistants to assess inter-rater reliability. Transcripts rated by both coders
were matched utterance by utterance. Utterance codes forming MI-consistent and MI-inconsistent
counselor behaviors were combined separately for ease of interpretation. The cross-tabulated
frequencies of counselor and client utterance counts given in Table 2 indicate a high agreement between coders. The contingency coefficient—a nonparametric
equivalent of the correlation coefficient—was calculated for reliability since Cohen’s
kappa was undefined due to the nature of the data (i.e., not every category existed
for both set of coders). The overall contingency coefficient was C = 0.91, suggesting excellent inter-rater reliability [47]. Table 2 reports each rater’s frequency codes to reflect the inter-rater reliability.

Results

Participants

Descriptive findings for the changer and non-changer participants, along with the
entire MI cohort, are presented in Table 1. For the participants whose data was coded (n = 43), participants were mainly male (98%) and white (non-Hispanic, 95%), with a
mean age of 41 (SD = 7.46) at the start of the study. At baseline year, the average servings of fruits
and vegetables per day were 4.29 (SD = 1.76) and participants’ mean body mass index was 27.6 (SD = 3.8).

Given counselor MI-inconsistent behaviors did not correlate with our hypothesized
mediator, we did not perform mediation analyses using MI-inconsistent counselor behaviors.
Client sustain talk did not correlate significantly with any counselor scores or fruit
and vegetable consumption; we therefore dropped this variable from subsequent analyses.

Mediation analyses

Mediation analyses help to understand the underlying mechanisms of a phenomenon [48]. For example, a simple mediated effect occurs when an intervention changes a mediator
(i.e., the α path) and that mediator changes the outcome (i.e., the β path) (see Figure 1). The mediated effect is then the product of α and β paths, αβ, which estimates the part of the total program effect transmitted through the mediator.
The analysis of mediated effects in prevention programs can be sometimes problematic
if the sample size is small. In that case, the sampling distribution of the mediated
effect estimate can have a non-normal distribution, which leads to biased confidence
interval estimates. An approach to this problem is to use Bayesian analyses to estimate
the mediated effect, since a Bayesian approach does not require the assumption of
normality in the sampling distribution of estimates [49]. Another advantage of the Bayesian approach is that it allows the statistical incorporation
of prior findings into the statistical analysis. In Bayesian analyses, the unknown
parameters are treated as random variables with a distribution. The mediated effect
estimator is updated by using the prior knowledge of estimators which are assumed
to have a probability distribution called the prior distribution. We used MPlus 6.0
software to conduct the Bayesian mediation analyses.

Given our significant correlational findings and the previous literature suggesting
a mediational relationship between counselor behaviors (MI-consistent behaviors and
spirit) and hypothesized mediator (total positive client change talk), and with the
mediator and the outcome variable (fruit and vegetable consumption), we performed
a mediation analysis examining whether total positive client change talk mediated
the relationship between counselor behaviors (MI-consistent behaviors and spirit)
and fruit/vegetable consumption (See Figure 1). Due to the limited sample size, we analyzed each hypothesized mediated effect separately.
To clarify, these mediation analyses were not examining the mediators of the overall
intervention (i.g., MI intervention versus control) but instead mediators of the efficacy
of the MI intervention on fruit-vegetable consumption.

Bayesian mediation analysis

Bayesian mediation analyses allow for the incorporation of prior research. Apodaca
& Longabaugh’s [50] meta-analysis indicated that the pooled effect size for MI counselor behavior and
client behavior was r = .46, and that the pooled effect size for client behavior and outcome variables
was r = .23. In a mediated effect, correlation coefficients are suggested as appropriate
effect size estimates of individual paths [51]. Consequently, we used the above correlation coefficients as informative priors in
the Bayesian mediation analyses (MPLUS syntax provided in Appendix A).

First, we performed a Bayesian mediation analysis with total positive client change
talk mediating the relationship between MI-consistent counselor behaviors and fruit/vegetable
consumption. The posterior mean of the mediated effect of counselor MI-consistent
behaviors through total positive client change talk on change in fruit/vegetable consumption
was αβ = .06 (.03) with a 95% credible interval [.02, .12]. This supports the hypothesis
that the effect of counselor MI-consistent behavior on the outcome variable is mediated
through total positive client change talk. MI-consistent counselor behaviors predicted
an increase in total positive client change talk, and in turn, total positive client
change talk increas ed fruit and vegetable consumption.

Next, we performed a Bayesian mediation analysis of total positive client change talk
mediating the relationship between counselor spirit and fruit/vegetable intake. A
similar pattern was observed for counselor spirit: the posterior mean of the mediated
effect of counselor spirit through total positive client change talk on change in
fruit and vegetable consumption was αβ = .06 (.03) with a 95% credible interval [.01, .13]. Counselor spirit increased total
positive client change talk, and client total positive client change talk increased
fruit and vegetable consumption.

The significant mediation paths for both sets of Bayesian analyses are illustrated
in Figure 1, with Table 5 listing the path estimates for the mediation analyses.

Frequentist (non-Bayesian) mediation analysis

We also conducted conventional frequentist mediation analyses to compare the estimates
from the two approaches, using PRODCLIN [52] to calculate the 95% asymmetric confidence limits for each mediated effect. The PRODCLIN
program provides more accurate confidence limits for the mediated effect since the
distribution of the product of two normally distributed variables is not normal [52].

The mediated effect of counselor MI-consistent behaviors through total positive client
change talk on change in fruit and vegetable consumption was significant: αβ = .05 (.02) with a 95% confidence interval of [.01, .10], as was the mediated effect
of counselor’s MI spirit through total positive client change talk on change in fruit
and vegetable consumption: αβ = .37 (.23) with a 95% confidence interval of [.003, .89], both consistent with the
Bayesian mediation analyses. These results suggest that total positive client change
talk mediates the relationship between MI-consistent counselor behaviors and spirit
and the outcome variable—increased fruit and vegetable consumption.

The standard errors are slightly smaller and confidence intervals narrower when using
the Bayesian approach compared with the frequentist approach. However, the meaning
of the interval differs by method. A 95% Bayesian credible interval suggests that
there is a 95% chance that the credible interval contains the true value of the coefficient
based on the observed data, whereas a 95% frequentist confidence interval means that
if we repeatedly sample from a population and calculated the confidence interval for
each sample, on average 95% of those intervals contain the true value of the estimate.
In this sense, Bayesian credible intervals are more intuitive than conventional confidence
intervals, since credible intervals rely on a more meaningful probability distribution
rather than an idealistic assumption of repeated sampling under identical conditions
[49].

Discussion

Overall, our findings are that counselor behavior (MI-consistent behaviors and spirit)
predicts firefighters’ expressions of intentions to make positive changes, and those
expressions in turn predict increased future fruit and vegetable intake. This parallels
findings with the use of MI for stopping harmful health behaviors (e.g., alcohol use
and smoking) and also supports both components of the proposed theoretical structure
of MI [34]. MI spirit has been suggested as a critical feature in its efficacy [53], as it relates to the tenant that an empathic relationship displaying positive regard
can result in favorable outcomes [54-56]. Previously, the mediational link between spirit and client behavior change has been
found for alcohol [37] and smoking [57]. However, those are both situations where the guilt associated with the action being
addressed might heighten the adversarial role of counselor and client, augmenting
the importance of positive regard. Finding a relationship with spirit when addressing
fruit and vegetable intake highlights the importance of collaboration and honoring
autonomy.

For counselor behaviors and overall spirit, total positive client change talk was
the mediating variable, and has become an important construct in MI. Proposed as an
instrumental factor in 2002 [58], its role has been supported by the work of investigators using sequential analysis
of MI interactions with alcohol use [35,38]. Our findings corroborate those findings and extend its relevance to promoting healthy
behaviors. Client change talk’s role also supports MI’s proposed underpinnings in
self-perception theory [36,59] that suggest the counterintuitive sequence of an individual’s behaviors affect their
attitudes, as in this case they may talk themselves into changing. Finding that total
positive client change talk mediated behavioral outcomes underscores the importance
of MI counselors’ use of techniques to facilitate those expressions.

Moyers et al. [35] tested a directional model of motivational interviewing efficacy for an alcohol cessation
intervention, whereby MI-consistent counselor behaviors elicited participants’ client
change talk 17% of the time and suppressed participants’ sustain talk. This provides
specific directional evidence suggesting counselor behaviors evoke client responses,
rather than client responses eliciting counselor behaviors or a bidirectional model
of causality. Using this framework, we tested whether counselor behaviors elicited
change in total positive client change talk which in turn predicted change in fruit
and vegetable consumption. Our findings are consistent with total positive client
change talk as a mediator of the relationship between counselor behaviors and fruit
and vegetable intake. Although we did not use sequential analyses of counselor and
client utterances (which provide temporal evidence of the causal relationship between
predictor and mediator variables), our outcome variable was assessed at a second time
point. This provides some temporal evidence for client and counselor behaviors eliciting
changes in fruit and vegetable consumption.

MI has been successful in substance use intervention research [22,27-29] yet its use in health promotion research has not yet been extensively studied [30]. Psychological factors underlying cessation interventions potentially enhance the
importance of the intervention and thus augment its efficacy. It is therefore important
to test MI’s success in health promotion interventions as the strength of the motivation
to change might differ and undercut the effectiveness. Our results confirm relationships
previously identified with stopping harmful behaviors also apply to MI use with promoting
healthy actions.

An interesting and perhaps counterintuitive finding was the positive correlation between
MI-inconsistent behaviors and fruit and vegetable consumption. However, MI-inconsistent
behaviors occurred infrequently, included giving advice or directing without permission,
rather than warning or confronting (see Table 3), and did not correlate with total positive client change talk. One could speculate
that unsolicited advice on increasing fruit and vegetable intake may be helpful for
increasing the behavior but because of the low frequency it is important to not overstate
this finding. That MI-inconsistent behaviors were unrelated to client positive change
talk suggests MI-inconsistent behaviors did not elicit spontaneous client utterances
of intentions to change behaviors, but still increased fruit and vegetable consumption.
An interesting future direction might be to examine the relationship between MI-inconsistent
counselor behaviors and long-standing changes.

Limitations & future directions

Our participants were mainly white males, which may limit the generalization of our
findings; however, efficacy trials indicate that MI effect sizes may be greater in
minority populations [20,60]. In addition, our sample size was relatively small, which can hinder tests of mediation
particularly because the mediated effect estimate has a non-normal distribution. Furthermore,
we used purposeful sampling to identify a subset of participants for which a range
of change occurred on the outcome variable, and so therefore it is also possible that
confounders may have affected the mediation analysis. To overcome our limited sample
size, we used a novel approach—Bayesian mediation analyses (which does not require
normal distributions of the estimates [49])—to investigate mediation. Bayesian mediation analyses supplements traditional mediation
analyses in small sample sizes by using prior information on the parameter as well
as making fewer assumptions about the distribution of the mediated effect. Even given
our sample, total positive client change talk emerged as a significant mediator between
counselor behaviors and fruit and vegetable consumption, thus suggesting the robustness
of the results.

An additional limitation is that our coding data came from a single interaction between
the counselor and the firefighter. Recent work on client change talk has focused on
the trajectory of change within and across sessions [41,61], and this trajectory has been found to be important, with differences emerging over
time [33]. However, identifying mediation effects from the initial interaction in which clients
discussed their assessment demonstrates the robustness of the findings. Perhaps the
conversation following the discussion of the firefighters’ assessment presents the
strongest opportunity for eliciting client change talk—change talk that has the strongest
and lasting impact on health behaviors. Future research can compare the differential
impact of the motivational interviewing immediately after the results assessment (session
two) with the other follow-up sessions to determine the critical sessions for behavior
change in the motivational interviewing intervention.

Conclusion

Motivational interviewing as an intervention is a resource- and time-intensive process,
and it is currently being applied in many arenas. Therefore it is imperative to understand
the mechanisms of motivational interviewing’s effectiveness to inform MI training
and application, ideally identifying those resource and time intensive processes critical
for MI success in health promotion domains.

ANALYSIS

Model Priors

Model Constraint

new (indirect);indirect = a*b;

OUTPUT

! Tech8 gives scale reduction factor;tech1 tech8 standardized;

PLOT

! Plot2 gives the times series (trace) plots for each parameter;type = plot2;

Competing interests

The PHLAME program is listed on the Cancer Control P.L.A.N.E.T. website of evidence-based
programs, and the PHLAME team program is distributed through the Center for Health
Promotion Research at Oregon Health & Science University (OHSU). OHSU and Dr. Elliot
have a financial interest from the commercial sale of technologies used in this research.
This potential conflict of interest has been reviewed and managed by the OHSU Conflict
of Interest in Research Committee.

Authors’ contributions

DE and CAD conducted the original study; YK-S and DPM performed the statistical analyses;
and AGP, YK-S, and DPM drafted the manuscript. All authors contributed to and approved
the final manuscript.

Acknowledgements

This research was supported by the National Institutes of Health R01AR45901, R01CA105774,
and 5RC1NR011793. We also gratefully acknowledge the contributions of Mary Eash, Chondra
M. Lockwood, PhD, Wendy McGinnis, MA, and Linda Nebeling, PhD, MPH, RD.

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